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Quantitative proteomics analysis identifies MUC1 as an effect sensor of EGFR inhibition

Abstract

Tumor responses to cancer therapeutics are generally monitored every 2–3 months based on changes in tumor size. Dynamic biomarkers that reflect effective engagement of targeted therapeutics to the targeted pathway, so-called “effect sensors”, would fulfill a need for non-invasive, drug-specific indicators of early treatment effect. Using a proteomics approach to identify effect sensors, we demonstrated MUC1 upregulation in response to epidermal growth factor receptor (EGFR)-targeting treatments in breast and lung cancer models. To achieve this, using semi-quantitative mass spectrometry, we found MUC1 to be significantly and durably upregulated in response to erlotinib, an EGFR-targeting treatment. MUC1 upregulation was regulated transcriptionally, involving PI3K-signaling and STAT3. We validated these results in erlotinib-sensitive human breast and non-small lung cancer cell lines. Importantly, erlotinib treatment of mice bearing SUM149 xenografts resulted in increased MUC1 shedding into plasma. Analysis of MUC1 using serial blood sampling may therefore be a new, relatively non-invasive tool to monitor early and drug-specific effects of EGFR-targeting therapeutics.

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References

  1. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45:228–47.

    Article  CAS  Google Scholar 

  2. Lopez JS, Banerji U. Combine and conquer: challenges for targeted therapy combinations in early phase trials. Nat Rev Clin Oncol. 2017;14:57–66.

    Article  CAS  Google Scholar 

  3. de Vries EGE, THO Munnink, van Vugt MATM, Nagengast WB. Toward molecular imaging-driven drug development in oncology. Cancer Discov. 2011;1:25–8.

    Article  Google Scholar 

  4. Pool M, de Boer HR, Lub-de Hooge MN, van Vugt MATM, de Vries EGE. Harnessing integrative omics to facilitate molecular imaging of precision medicine of the human epidermal growth factor receptor family. Theranostics. 2017;7:2111–33.

    Article  CAS  Google Scholar 

  5. Vargas AJ, Harris CC. Biomarker development in the precision medicine era: lung cancer as a case study. Nat Rev Cancer. 2016;16:525–37.

    Article  CAS  Google Scholar 

  6. Petrylak DP, Ankerst DP, Jiang CS, Tangen CM, Hussain MHA, Lara PN, et al. Evaluation of prostate-specific antigen declines for surrogacy in patients treated on SWOG 99-16. J Natl Cancer Inst. 2006;98:516–21.

    Article  Google Scholar 

  7. Forshew T, Murtaza M, Parkinson C, Gale D, Tsui DWY, Kaper F, et al. Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Sci Transl Med. 2012;4:136ra68

    Article  Google Scholar 

  8. de Vries EGE, de Jong S, Gietema JA. Molecular imaging as a tool for drug development and trial design. J Clin Oncol. 2015;33:2585–7.

    Article  Google Scholar 

  9. Arteaga CL, Engelman JA. ERBB receptors: from oncogene discovery to basic science to mechanism-based cancer therapeutics. Cancer Cell. 2014;25:282–303.

    Article  CAS  Google Scholar 

  10. Mok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med. 2009;361:947–57.

    Article  CAS  Google Scholar 

  11. Cunningham D, Humblet Y, Siena S, Khayat D, Bleiberg H, Santoro A. et al. Cetuximab monotherapy and cetuximab plus irinotecan in irinotecan-refractory metastatic colorectal cancer. N Engl J Med. 2004;351:337–45.

    Article  CAS  Google Scholar 

  12. Kani K, Faca VM, Hughes LD, Zhang W, Fang Q, Shahbaba B, et al. Quantitative proteomic profiling identifies protein correlates to EGFR kinase inhibition. Mol Cancer Ther. 2012;11:1071–81.

    Article  CAS  Google Scholar 

  13. Hoadley KA, Weigman VJ, Fan C, Sawyer LR, He X, Troester MA, et al. EGFR associated expression profiles vary with breast tumor subtype. BMC Genomics. 2007;8:258

    Article  Google Scholar 

  14. Yamasaki F, Zhang D, Bartholomeusz C, Sudo T, Hortobagyi GN, Kurisu K, et al. Sensitivity of breast cancer cells to erlotinib depends on cyclin-dependent kinase 2 activity. Mol Cancer Ther. 2007;6:2168–77.

    Article  CAS  Google Scholar 

  15. Busse D, Doughty RS, Ramsey TT, Russell WE, Price JO, Flanagan WM, et al. Reversible G(1) arrest induced by inhibition of the epidermal growth factor receptor tyrosine kinase requires up-regulation ofp27(KIP1) independent of MAPK activity. J Biol Chem. 2000;275:6987–95.

    Article  CAS  Google Scholar 

  16. Ong S-E, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics. 2002;1:376–86.

    Article  CAS  Google Scholar 

  17. Wang S-C, Nakajima Y, Yu Y-L, Xia W, Chen C-T, Yang C-C, et al. Tyrosine phosphorylation controls PCNA function through protein stability. Nat Cell Biol. 2006;8:1359–68.

    Article  CAS  Google Scholar 

  18. Lee S-I, Batzoglou S. Application of independent component analysis to microarrays. Genome Biol. 2003;4:R76.

    Article  Google Scholar 

  19. Kufe DW. Mucins in cancer: function, prognosis and therapy. Nat Rev Cancer. 2009;9:874–85.

    Article  CAS  Google Scholar 

  20. Duffy MJ, Walsh S, McDermott EW, Crown J. Biomarkers in breast cancer: where are we and where are we going? Adv Clin Chem. 2015;71:1–23.

    Article  Google Scholar 

  21. Turke AB, Zejnullahu K, Wu Y-L, Song Y, Dias-Santagata D, Lifshits E et al. Preexistence and clonal selection of MET amplification in EGFR mutant NSCLC. Cancer Cell. 2010;17:77–88.

    Article  CAS  Google Scholar 

  22. Pool M, Terwisscha van Scheltinga AGT, Kol A, Giesen D, de Vries EGE, Lub-de Hooge MN et al. 89Zr-Onartuzumab PET imaging of c-MET receptor dynamics. Eur J Nucl Med Mol Imaging. 2017;44:1328–36.

    Article  CAS  Google Scholar 

  23. Geyer CE, Forster J, Lindquist D, Chan S, Romieu CG, Pienkowski T. et al. Lapatinib plus capecitabine for HER2-positive advanced breast cancer. N Engl J Med. 2006;355:2733–43.

    Article  CAS  Google Scholar 

  24. Sequist LV, Yang JC, Yamamoto N, O’Byrne K, Hirsh V, Mok T. et al. LUX-LUNG 3 Phase III study of afatinib or cisplatin plus pemetrexed in patients with metastatic lung adenocarcinoma with EGFR mutations. J Clin Oncol. 2013;31:1–11.

    Article  Google Scholar 

  25. White DE, Negorev D, Peng H, Ivanov AV, Maul GG, Rauscher FJ. et al. KAP1, a novel substrate for PIKK family members, colocalizes with numerous damage response factors at DNA lesions. Cancer Res. 2006;66:11594–9.

    Article  CAS  Google Scholar 

  26. Ren J, Agata N, Chen D, Li Y, Yu W, Huang L. et al. Human MUC1 carcinoma-associated protein confers resistance to genotoxic anticancer agents. Cancer Cell. 2004;5:163–75.

    Article  CAS  Google Scholar 

  27. Gilbert LA, Larson MH, Morsut L, Liu Z, Brar GA, Torres SE et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell. 2013;154:442–51.

    Article  CAS  Google Scholar 

  28. Kearns NA, Genga RMJ, Enuameh MS, Garber M, Wolfe SA, Maehr R et al. Cas9 effector-mediated regulation of transcription and differentiation in human pluripotent stem cells. Development. 2013;141:219–23.

    Article  Google Scholar 

  29. Gilbert LA, Horlbeck MA, Adamson B, Villalta JE, Chen Y, Whitehead EH. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell. 2014;159:647–61.

    Article  CAS  Google Scholar 

  30. Sergina NV, Rausch M, Wang D, Blair J, Hann B, Shokat KM et al. Escape from HER-family tyrosine kinase inhibitor therapy by the kinase-inactive HER3. Nature. 2007;445:437–41.

    Article  CAS  Google Scholar 

  31. Chakrabarty A, Sanchez V, Kuba MG, Rinehart C, Arteaga CL. Feedback upregulation of HER3 (ErbB3) expression and activity attenuates antitumor effect of PI3K inhibitors. Proc Natl Acad Sci USA. 2012;109:2718–23.

    Article  CAS  Google Scholar 

  32. Chandarlapaty S, Sawai A, Scaltriti M, Rodrik-Outmezguine V, Grbovic-Huezo O, Serra V, et al. AKT inhibition relieves feedback suppression of receptor tyrosine kinase expression and activity. Cancer Cell. 2011;19:58–71.

    Article  CAS  Google Scholar 

  33. Garrett JT, Olivares MJ, Rinehart C, Granja-Ingram ND, Sanchez V, Chakrabarty A, et al. Transcriptional and posttranslational up-regulation of HER3 (ErbB3) compensates for inhibition of the HER2 tyrosine kinase. Proc Natl Acad Sci USA. 2011;108:5021–6.

    Article  CAS  Google Scholar 

  34. Engelman JA, Zejnullahu K, Mitsudomi T, Song Y, Hyland C, Park JO, et al. MET amplification leads to gefitinib resistance in lung cancer by activating ERBB3 signaling. Science. 2007;316:1039–43.

    Article  CAS  Google Scholar 

  35. Gaemers IC, Vos HL, Volders HH, Van der Valk SW, Hilkens J. A STAT-responsive element in the promoter of the episialin/MUC1 gene is involved in its overexpression in carcinoma cells. J Biol Chem. 2001;276:6191–9.

    Article  CAS  Google Scholar 

  36. Ahmad R, Rajabi H, Kosugi M, Joshi MD, Alam M, Vasir B. et al. MUC1-C oncoprotein promotes STAT3 activation in an autoinductive regulatory loop. Sci Signal. 2011;4:ra9

    Article  Google Scholar 

  37. Kovarik A, Lu PJ, Peat N, Morris J, Taylor-Papadimitriou J. Two GC boxes (Sp1 sites) are involved in regulation of the activity of the epithelium-specific MUC1 promoter. J Biol Chem. 1996;271:18140–7.

    Article  CAS  Google Scholar 

  38. Starsíchová A, Lincová E, Pernicová Z, Kozubík A, Soucek K. TGF-beta1 suppresses IL-6-induced STAT3 activation through regulation of Jak2 expression in prostate epithelial cells. Cell Signal. 2010;22:1734–44.

    Article  Google Scholar 

  39. Ueno NT, Zhang D. Targeting EGFR in triple negative breast cancer. J Cancer. 2011;2:324–8.

    Article  CAS  Google Scholar 

  40. Lee HJ, Zhuang G, Cao Y, Du P, Kim HJ, Settleman J, et al. Drug resistance via feedback activation of stat3 in oncogene-addicted cancer cells. Cancer Cell. 2014;26:207–21.

    Article  CAS  Google Scholar 

  41. Aebersold R, Mann M. Mass-spectrometric exploration of proteome structure and function. Nature. 2016;537:347–55.

    Article  CAS  Google Scholar 

  42. Savage P, Blanchet-Cohen A, Revil T, Badescu D, Saleh SMI, Wang YC, et al. A targetable EGFR-dependent tumor-initiating program in breast cancer. Cell Rep. 2017;21:1140–9.

    Article  CAS  Google Scholar 

  43. Pool M, Kol A, Lub-de Hooge MN, Gerdes CA, de Jong S, de Vries EGE, et al. Extracellular domain shedding influences specific tumor uptake and organ distribution of the EGFR PET tracer 89Zr-imgatuzumab. Oncotarget. 2016;7:68111–21.

    Article  Google Scholar 

  44. Ishikawa N, Hattori N, Yokoyama A, Tanaka S, Nishino R, Yoshioka K, et al. Usefulness of monitoring the circulating Krebs von den Lungen-6 levels to predict the clinical outcome of patients with advanced nonsmall cell lung cancer treated with epidermal growth factor receptor tyrosine kinase inhibitors. Int J Cancer. 2008;122:2612–20.

    Article  CAS  Google Scholar 

  45. Fujiwara Y, Kiura K, Toyooka S, Hotta K, Tabata M, Takigawa N, et al. Elevated serum level of sialylated glycoprotein KL-6 predicts a poor prognosis in patients with non-small cell lung cancer treated with gefitinib. Lung Cancer. 2008;59:81–7.

    Article  Google Scholar 

  46. Bearz A, Talamini R, Vaccher E, Spina M, Simonelli C, Steffan A, et al. MUC-1 (CA 15-3 antigen) as a highly reliable predictor of response to EGFR inhibitors in patients with bronchioloalveolar carcinoma: an experience on 26 patients. Int J Biol Markers. 2008;22:307–11.

    Google Scholar 

  47. D’Alessandro R, Roselli M, Ferroni P, Mariotti S, Spila A, Aloe S, et al. Serum tissue polypeptide specific antigen (TPS): a complementary tumor marker to CA 15-3 in the management of breast cancer. Breast Cancer Res Treat. 2001;68:9–19.

    Article  Google Scholar 

  48. Heijink AM, Blomen VA, Bisteau X, Degener F, Matsushita FY, Kaldis P. et al. A haploid genetic screen identifies the G 1/S regulatory machinery as a determinant of Wee1 inhibitor sensitivity. Proc Natl Acad Sci USA. 2015;112:15160–5.

    Article  CAS  Google Scholar 

  49. Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26:1367–72.

    Article  CAS  Google Scholar 

  50. Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M, et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011;10:1794–805.

    Article  CAS  Google Scholar 

  51. Vizcaíno JA, Côté RG, Csordas A, Dianes JA, Fabregat A, Foster JM. et al. The proteomics identifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 2013;41:D1063–9.

    Article  Google Scholar 

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Acknowledgements

We thank members of the Medical Oncology Department and Cancer Research Center Groningen for helpful discussions. This work is financially supported by the European Research Council (ERC-Advanced Grant ERC-2011-293-445 to E.G.E.d.V.). E.G.E.d.V. and M.A.T.M.v.V. conceived the study. H.R.d.B., E.J., S.v.C., and M.E. designed and performed in vitro experiments. E.J. and F.F. performed MS experiments and analysis. R.S.N.F. and H.R.d.B. performed pathway analysis. M.P., D.F.S., and W.H. designed and manufactured molecular imaging tools. H.R.d.B. and M.P. performed in vivo experiments. H.R.d.B., E.G.E.d.V., and M.A.T.M.v.V. wrote the manuscript and all authors contributed to editing of the manuscript. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository 51 with the dataset identifier PXD005985.

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Correspondence to Marcel A. T. M. van Vugt.

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de Boer, H.R., Pool, M., Joosten, E. et al. Quantitative proteomics analysis identifies MUC1 as an effect sensor of EGFR inhibition. Oncogene 38, 1477–1488 (2019). https://doi.org/10.1038/s41388-018-0522-7

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